Current medical imaging devices and a pre- and post-processing equipment generate, aside from the actual image data, a growing quantity of useful data, which can supplement the image data and be used for a further processing. This supplementary data is currently only used in the closed low-level system components within the medical imaging device and is not employed by specialized high-level algorithms and applications in external processing devices.
Medical imaging devices generate a large quantity of measurement data. In the case of CT scanners, measurement data of this type comprises sinograms which are obtained at different spatial, temporal or energy locations. This supplementary data (rich data or augmented data) is processed internally in the medical imaging device, while only standardized image reconstructions are provided in the form of DICOM image series for external processing devices. The supplementary data is not made accessible to the external processing devices which are connected to the medical imaging device.
Different algorithms of external processing devices would however be able to further process not only image data but also the supplementary data, for an image processing for instance. In the case of a CT scanner, the supplementary data may comprise different data relating to dual-energy classes, a spectral imaging, an iterative reconstructed imaging, a segmentation and labeled data. The labeled data identifies specific organs or anatomical areas in the associated image data for instance.
Typically the supplementary data is processed internally by the medical imaging device and is not transferred to the external processing devices. The performance capability of data-dependent applications and algorithms of artificial intelligence nevertheless depends heavily on the quality and quantity of data, which is used as input parameters.